International journal of epidemiology最新文献

Background: Earlier studies have suggested that SARS-CoV-2 infection increases the risk of subsequently developing diabetes. However, reports are more limited and inconsistent for infection with the Omicron variant and how COVID-19 vaccination may alter the risks.

Methods: A population-based, matched cohort study was conducted by using linked registry data. Individuals aged ≥16 years diagnosed with COVID-19 between 15 December 2021 and 31 December 2022 were matched to those without COVID-19 by age, sex, and COVID-19 vaccine recency. Cause-specific Cox models were used to estimate the association between COVID-19 and the initiation of diabetes treatment, adjusting for relevant demographic, healthcare-utilization, and health-related factors. Negative control outcomes were assessed.

Results: Among 5 736 501 matched pairs with and without COVID-19, followed for a median of 200 days, 45 816 initiated diabetes treatment. Compared with those without COVID-19, individuals with COVID-19 had a 14% higher risk of subsequently initiating diabetes treatment [adjusted hazard ratio (aHR) 1.14; 95% confidence interval (CI) 1.12; 1.17]. The risk was higher for those who had received up to two COVID-19 vaccine doses compared with those who had received a booster within the last 90 days (aHR 1.22; 95% CI 1.18, 1.25 vs aHR 1.08; 95% CI 1.04, 1.12). Similar associations were observed between COVID-19 and negative control outcomes while the incidence patterns differed.

Conclusion: While we observed a small increased risk of diabetes following SARS-CoV-2 infection during the Omicron-dominant period, the true causal effect could be small or even null given the potential unmeasured confounding and detection bias. Future research on post-acute COVID-19 outcomes should consider including negative control outcomes to better detect potential biases.

Background: Healthy worker survivor bias (HWSB) skews health outcome studies by favouring healthier employed individuals. While advanced statistical methods exist, their application in Korea has been limited due to insufficient occupational and mortality data. This study quantifies HWSB due to employment status changes (HWSB-ES) using Korea's National Health Insurance Service (NHIS) database.

Methods: This retrospective cohort study analysed NHIS data to assess HWSB-ES in individuals aged 30-59 years who maintained consistent insurance types from 2008 to 2010. The primary outcome, all-cause mortality, was tracked until December 2022. Insurance type determined employment status, with industry details collected for employees. Landmark analysis (origin: 2011; current: 2012-21) estimated HWSB-ES by assessing mortality risk attenuation in fixed and dynamic cohorts, stratified by age, sex, and landmark periods (1-10 years for short-term; 1-7 years for long-term).

Results: After exclusions, 18 192 989 participants were included (median age: 44 years; 49.05% male). HWSB-ES was more pronounced in female, dynamic cohorts, and longer landmark periods. Importantly, the effect of HWSB-ES intensified with age but showed a smaller long-term attenuation compared to the short-term effect. Short-term HWSB-ES attenuated mortality risk by 25%-30% in male and 36%-39% in female. Long-term attenuation was lower, at 7%-15% in male and 12%-18% in female.

Conclusions: The quantified HWSB-ES results provide critical national-level estimates for adjustment, especially in female and older cohorts, to prevent the underestimation of adverse health effects in occupational research.

Background: The burden of non-communicable diseases (NCDs) in adolescent and young adult females in sub-Saharan Africa (SSA) has not been comprehensively studied. To address this gap, we analysed data from the Global Burden of Diseases (GBD) 2021, focusing on death due to NCDs in females aged 10-24 years in SSA.

Methods: We extracted data from GBD 2021 on NCD deaths in females aged 10-24 years in SSA from 2000 to 2021. We presented the numbers and death rates of NCDs, and the proportion of NCDs in all-cause deaths was calculated. Pearson's correlation was applied to explore the NCD burden on the socioeconomic development and health system. Additionally, we projected the NCD burden until 2050 by applying mixed-effects models.

Results: In 2021, 52 083.13 (42 018.18∼61 630.88) NCD deaths, at a mortality rate of 27.59 (22.26∼32.64) per 100 000 population, emerged, accounting for 21.13% (17.57%∼24.22%) of the total deaths. Neoplasms, cardiovascular diseases, digestive diseases, neurological disorders, and diabetes and kidney diseases were the top five leading causes of deaths. Inverse associations were observed between the NCD death rates and indicators of the socioeconomic and health system (P < .001). An increasing trend was observed of the NCD death numbers and the contributing proportions since 2000, and it was predicted to continue increasing through to 2050, with the highest increasing trend in neoplasms.

Conclusion: The rising disease burden of NCDs for adolescent and young adult females in SSA has attracted attention. Targeted interventions and strengthened health systems should be prioritized to address the concerning NCD burden in adolescent girls in SSA.

Background: During the perinatal period, the fetus can exert profound effects on processes that alter pre- and postnatal maternal physiology. It is possible to investigate the causal effect of offspring perinatal exposures on their mother's health using Mendelian randomization (MR). However, analyses need to be adjusted for maternal genotype to avoid confounding. Such analyses are difficult to perform at scale because of the paucity of cohorts across the world with large numbers of genotyped maternal-offspring dyads and parent-offspring trios.

Methods: We introduce the "offspring genotype-by-proxy" MR framework which can be employed in the absence of offspring genetic information to complement existing approaches in the triangulation of causal inference. The basic idea is to use paternal genotypes to proxy the direct effect of their offspring's genotype on their offspring's own exposures.

Results: We compare our framework to other MR designs and investigate the consequences of model misspecification and spousal misclassification on statistical power, consistency, and bias. In addition, we discuss the key MR assumptions that prevent these approaches from being appropriate for investigating the effect of many offspring postnatal and later life exposures on maternal health.

Conclusion: Given the increasing availability of datasets such as the UK Biobank that (incidentally) include tens of thousands of genome-wide genotyped spousal pairs and large population biobanks with linked health record data for first-degree relatives, the offspring genotype-by-proxy MR approach could augment causal analyses of offspring perinatal exposures on their mother's outcomes as implementation is not restricted to datasets with mother-offspring genotype information.

Introduction: In air-pollution epidemiology, measured or modelled surrogate exposure estimates, prone to measurement error (ME), are used to investigate the health effects of exposure to pollution of outdoor origin, potentially leading to biased effect estimates. We predicted the annual personal exposure from outdoor sources by using personal measurements, compared it with concentrations from surrogate metrics, and quantified the ME magnitude, type, and determinants.

Methods: We used measurements from four panel studies in London, UK, and predicted personal exposures to fine particulate matter (PM2.5), nitrogen dioxide (NO2), ozone (O3), and black carbon (BC). We compared those with surrogate exposures, including measurements from fixed-site monitors, modelled ambient concentrations, or hybrid methods accounting for people's mobility. We estimated the exposure ME magnitude, correlations, and variance ratios between surrogate measures and personal exposure, and the percentages of classical/Berkson-type errors. Individual- and area-level characteristics, such as age, sex, socio-economic status, and time spent outdoors, were assessed as potential error determinants.

Results: Predicted annual personal exposures to PM2.5, NO2, O3, and BC from outdoor sources were overestimated by surrogate metrics, with mean differences of up to 10.1, 40.0, 61.7, and 2.6 μg/m3, respectively. The variance ratios and Pearson correlation coefficients between surrogate and predicted personal exposures ranged from 0.03 to 165.02 and -0.24 to 0.25. Time-activity adjustment reduced errors substantially. Berkson-type errors dominated the ME for PM2.5 and BC (43%-81% and 26%-98%, respectively), whilst classical errors characterized gases (>94% for both NO2 and O3). Time spent outdoors, house type, and deprivation were associated with exposure error.

Conclusion: The use of surrogate exposures to investigate the health effects of long-term exposure to air pollution from outdoor sources may bias the epidemiological estimates due to ME. Information about the error structures and their determinants can be used for correction and the identification of the true exposure-response functions.

Background: With rising longevity, multimorbidity is an increasingly important challenge for healthcare systems. We describe trends in the prevalence and incidence of multimorbidity across socioeconomic groups in Catalonia.

Methods: We use a random sample of 1 551 126 individuals (22% of the Catalan population, for whom we have the complete primary care health records) and follow them from 2010 until 2021. We document the age- and sex-specific prevalence and incidence of multimorbidity stratifying by income groups and birth cohorts. Logistic regression models are used to estimate the association between multimorbidity and mortality.

Results: Between 2010 and 2021, the prevalence of multimorbidity, higher among women, has increased for both sexes and all cohorts in our analysis. Importantly, each cohort attains the same ages, with higher multimorbidity prevalence than their predecessors had 10 years ago. Older generations are mostly affected by degenerative diseases, while younger age groups are more affected by mental health problems. Incidence tends to be higher among the older cohorts across all adult ages. We observe a strong socioeconomic gradient, with lower-income individuals experiencing worse multimorbidity prevalence and incidence. Such a gradient is persistent and becomes more pronounced at the end of the study period. Across all age groups, individuals experiencing multimorbidity have a higher risk of dying than those who do not.

Conclusion: The documented increases in multimorbidity alongside its socioeconomic gradients suggest that preventive strategies are urgently needed to defer or prevent its onset and slow its progression-especially among younger generations.

Background: Statins lower low-density lipoprotein cholesterol (LDL-C) and reduce the risk of coronary artery disease (CAD). However, they also increase the risk of type 2 diabetes (T2D).

Methods: We consider genetic variants in the region of the HMGCR gene, which encodes the target of statins, and their associations with downstream consequences of statins. We use various statistical methods to identify causal pathways influencing CAD and T2D, and investigate whether these are the same or different for the two diseases.

Results: Colocalization analyses indicate that LDL-C and body mass index (BMI) have distinct genetic predictors in this gene region, suggesting that they do not lie on the same causal pathway. Multivariable Mendelian randomization analyses restricted to variants in the HMGCR gene region revealed LDL-C and BMI as causal risk factors for CAD, and BMI as a causal risk factor for T2D, but not LDL-C. A Bayesian model averaging method prioritized BMI as the most likely causal risk factor for T2D, and LDL-C as the second most likely causal risk factor for CAD (behind ubiquinone). Colocalization analyses provided consistent evidence of LDL-C colocalizing with CAD, and BMI colocalizing with T2D; evidence was inconsistent for colocalization of LDL-C with T2D, and BMI with CAD.

Conclusions: Our analyses suggest cardiovascular and metabolic consequences of statin usage are on different causal pathways, and hence could be influenced separately by targeted interventions. More broadly, our analysis workflow offers potential insights to identify pathway-specific causal risk factors that could provide possible repositioning or refinement opportunities for existing drug targets.